# Feature selection for time-series

I am new to ML and was exploring a time-series dataset for the very first time. The aim was to predict the volume of vehicles passing one of the 4 junctions given some historical data. After dividing the DateTime column into separate columns like (Year,Month,Day etc), I started performing visualizations to see the trend of target variable(volume of vehicles in a junction) in this case and found out that the mean volume of vehicles more or less remain the same over some of the features. Please refer image below(Mean of vehicles passing 4 junctions/day): My question is that if we observe that a particular feature is not effecting the target variable much, can we directly assume that this feature will not be helpful to a ML model(as my intuition suggests) or am I completely wrong with my thinking?

We can do the subset selection instead of considering one feature at a time. We consider all the subset of features. And do the experiment on each of the subsets and choose the best subset of features. But this takes a long time as there are $$2^p$$ possibilities (p: number of features). So they start to develop more efficient algorithms to find an approximately best subset usually using a greedy algorithm.